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The Dynamics of Social Innovation

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The Dynamics of Social Innovation The dynamics of social innovation H. Peyton Young1 Department of Economics, University of Oxford, Oxford OX1 3UQ, United Kingdom Edited by Paul Robert Milgrom, Stanford University, Stanford, CA, and approved June 16, 2011 (received for review January 18, 2011) Social norms and institutions are mechanisms that facilitate co- innovations, because they require coordinated change in expect- ordination between individuals. A social innovation is a novel ations and behaviors by multiple individuals. For example, an mechanism that increases the welfare of the individuals who individual who invents a new form of legal contract cannot adopt it compared with the status quo. We model the dynamics simply institute it on his own: First, the other parties to the of social innovation as a coordination game played on a network. contract must enter into it, and second, the ability to enforce the Individuals experiment with a novel strategy that would increase contract will depend on its acceptance in society more generally. their payoffs provided that it is also adopted by their neighbors. Institutions exhibit strongly increasing returns precisely because The rate at which a social innovation spreads depends on three of their function as coordination technologies. This property is of factors: the topology of the network and in particular the extent course not limited to social innovations; it is also a feature of to which agents interact in small local clusters, the payoff gain of technological innovations with positive network externalities (4– the innovation relative to the status quo, and the amount of 6), and the results in this paper apply to these situations as well. noise in the best response process. The analysis shows that local The increasing returns feature of social innovation has im- clustering greatly enhances the speed with which social innova- portant implications for the dynamics governing institutional tions spread. It also suggests that the welfare gains from in- change. Of particular relevance is the social network that governs novation are more likely to occur in large jumps than in a series of people’s interactions. The reason is that, when a social in- small incremental improvements. novation first appears, it will typically gain a foothold in a rela- tively small subgroup of individuals that are closely linked by convergence time | learning | game dynamics geography or social connections. Once the new way of doing things has become firmly established within a local social group, ocial institutions are forms of capital that, together with it propagates to the rest of society through the social network. Sphysical and human capital, determine the rate of economic Thus, a key determinant of the speed with which institutional growth. Notable examples include private property rights and change occurs is the network topology and in particular the extent legal systems to protect them, accounting methods, forms of to which interactions are “localized”. corporate governance, and the terms of economic contracts. The relationship between speed of diffusion and network However, in contrast with the literature on technological prog- structure has been investigated in a variety of settings. One branch ress, relatively little is known about the ways in which new of the literature is concerned with dynamics in which each agent institutions are created and how they become established within responds deterministically to the number of his neighbors who a given social framework. In this paper I discuss one approach have adopted the innovation. From an initial seeding among a to this problem using methods from evolutionary game theory. subset of agents, the question is how long it takes for the inno- In the abstract, an institution can be viewed as a set of rules vation to spread as a function of network structure (7–11). A that structure a given type of interaction between individuals (1). second branch of the literature explores dynamics in which ’ These rules can be simple coordination devices, such as which agents respond to their neighbors choices probabilistically; that is, hand to extend in greeting or who goes through the door first. Or actions with higher expected payoffs are more likely to be chosen they can be very elaborate, such as rituals of courtship and than actions with low expected payoffs. Here the question is how marriage, cycles of retribution, performance criteria in employ- long it takes in expectation for the innovation to spread in the – ment contracts, and litigation procedures in the courts. We want population at large (12 15). The rate of spread hinges on topo- to know how a particular set of rules becomes established as logical properties of the network that are different from those common practice and what process describes the displacement of that determine the speed of convergence in the nonstochastic one set of rules by another. case. A third branch of the literature examines the issue of how The viewpoint we adopt here is that new norms and institu- the network structure itself evolves as agents are matched and – tions are introduced through a process of experimentation by rematched to play a given game (16 19). In this paper we assume that agents adjust their behavior individuals and that the value of adoption by a given individual is fi an increasing function of the number of his neighbors who have probabilistically and that the network structure is xed. In con- adopted. Under certain conditions that we discuss below, this trast to earlier work in this vein, however, we emphasize that it is trial-and-error process will eventually trigger a general change in not only the network topology that determines how fast an in- expectations and behaviors that establishes the new institution novation spreads. Rather, the speed depends on the interaction within society at large. However, it can take a very long time for between three complementary factors: i) the payoff gain repre- this to happen even when the new way of doing things is superior sented by the innovation in relation to the status quo; ii) the responsiveness or rationality of the agents, that is, the probability to the status quo. One reason for this inertia is lack of information: it may not be immediately evident that an innovation actually is superior to This paper results from the Arthur M. Sackler Colloquium of the National Academy of the status quo, due to the small number of prior instances and Sciences, “Dynamics of Social, Political, and Economic Institutions,” held December 3–4, variability in their outcomes. Thus, it may take a long time for 2010, at the Arnold and Mabel Beckman Center of the National Academies of Sciences enough information to accumulate before it becomes clear that and Engineering in Irvine, California. The complete program and audio filesofmost the innovation is superior (2). A second reason is that an inno- presentations are available on the NAS Web site at www.nasonline.org/Sackler_Dynamics. vation as initially conceived may not work very well in practice; it Author contributions: H.P.Y. performed research and wrote the paper. must be refined over time through a process of learning by doing The author declares no conflict of interest. (3). A third reason is that social innovations often exhibit in- This article is a PNAS Direct Submission. creasing returns. Indeed this property is characteristic of social 1E-mail: [email protected]. www.pnas.org/cgi/doi/10.1073/pnas.1100973108 PNAS Early Edition | 1of7 that they choose a (myopic) best response given their infor- bounded (possibly large) size that has a sufficiently high mation; and iii) the presence of small autonomous enclaves where proportion of its interactions with other members of the the innovation can gain an initial foothold, combined with group as opposed to outsiders. Various natural networks pathways that allow the innovation to spread by contagion once have this property, including those in which agents are em- it has gained a sufficient number of distinct footholds. We also bedded more or less uniformly in a finite Euclidean space use a somewhat subtle notion of what it means for an innovation and are neighbors if and only if they are within some spec- to “spread quickly”. Namely, we ask how long it takes in expec- ified distance of one another. (This result follows from tation for a high proportion of agents to adopt the innovation Proposition 2 below; a similar result is proved in ref. 15.) and stick with it with high probability. This latter condition is 4. The payoff gain from the innovation relative to the status needed because, in a stochastic learning process, it is quite pos- quo has an important bearing on the absolute speed with sible for an innovation to spread initially, but then go into reverse which it spreads. We call this the advance in welfare repre- and die out. We wish to know how long it takes for an innovation sented by the innovation. If the advance is sufficiently large, to reach the stage where it is in widespread use and continues to no special topological properties of the network are re- be widely used with high probability thereafter. quired for fast learning: It suffices that the maximum degree The main results of this paper can be summarized as follows. is bounded. First we distinguish between fast and slow rates of diffusion. 5. An innovation that leads to a small advance will tend to take Roughly speaking, an innovation spreads quickly in a given class much longer to spread than an innovation with a large ad- of networks if the expected waiting time to reach a high level of vance. A consequence is that successful innovations will penetration and stay at that level with high probability is bounded tend to occur in big bursts, because a major advance will- above independently of the number of agents; otherwise the in- tend to overtake prior innovations that represent only minor novation spreads slowly.
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